Laser induced forward transfer imaging using deep learning
Laser induced forward transfer imaging using deep learning
A novel approach for improving the accuracy and efficiency of laser-induced forward transfer (LIFT), through the application of deep learning techniques is presented. By training a neural network on a dataset of images of donor and receiver substrates, the appearance of copper droplets deposited onto the receiver was predicted directly from images of the donor. The results of droplet image prediction using LIFT gave an average RMSE of 9.63 compared with the experimental images, with the SSIM ranging from 0.75 to 0.83, reflecting reliable structural similarity across predictions. These findings underscore the model's predictive potential while identifying opportunities for refinement in minimising error. This approach has the potential to transform parameter optimisation for LIFT, as it enables the visualization of the deposited material without the time-consuming requirement of removing the donor from the setup to allow inspection of the receiver. This work therefore represents an important step forward in the development of LIFT as an additive manufacturing technology to create complex 3D structures on the microscale.
3D printing, Copper printing, Deep learning, LIFT, Laser induced forward transfer, Metal printing
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
Zervas, Michalis N.
1840a474-dd50-4a55-ab74-6f086aa3f701
Mills, Ben
05f1886e-96ef-420f-b856-4115f4ab36d0
April 2025
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
Zervas, Michalis N.
1840a474-dd50-4a55-ab74-6f086aa3f701
Mills, Ben
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James A., Zervas, Michalis N. and Mills, Ben
(2025)
Laser induced forward transfer imaging using deep learning.
Discover Applied Sciences, 7 (4), [254].
(doi:10.1007/s42452-025-06679-x).
Abstract
A novel approach for improving the accuracy and efficiency of laser-induced forward transfer (LIFT), through the application of deep learning techniques is presented. By training a neural network on a dataset of images of donor and receiver substrates, the appearance of copper droplets deposited onto the receiver was predicted directly from images of the donor. The results of droplet image prediction using LIFT gave an average RMSE of 9.63 compared with the experimental images, with the SSIM ranging from 0.75 to 0.83, reflecting reliable structural similarity across predictions. These findings underscore the model's predictive potential while identifying opportunities for refinement in minimising error. This approach has the potential to transform parameter optimisation for LIFT, as it enables the visualization of the deposited material without the time-consuming requirement of removing the donor from the setup to allow inspection of the receiver. This work therefore represents an important step forward in the development of LIFT as an additive manufacturing technology to create complex 3D structures on the microscale.
Text
LIFT_AI_21012025_clean_submitted
- Accepted Manuscript
Text
s42452-025-06679-x
- Version of Record
More information
Accepted/In Press date: 4 March 2025
e-pub ahead of print date: 22 March 2025
Published date: April 2025
Keywords:
3D printing, Copper printing, Deep learning, LIFT, Laser induced forward transfer, Metal printing
Identifiers
Local EPrints ID: 499762
URI: http://eprints.soton.ac.uk/id/eprint/499762
ISSN: 3004-9261
PURE UUID: f5df4206-3022-4dd6-ae42-caaa0ddf31a3
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Date deposited: 03 Apr 2025 16:40
Last modified: 22 Aug 2025 02:03
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Contributors
Author:
James A. Grant-Jacob
Author:
Michalis N. Zervas
Author:
Ben Mills
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